Prognostic biomarker replication factor C subunit 5 and its correlation with immune infiltrates in acute myeloid leukemia

ABSTRACT

Objective

To determine the role of replication factor C subunit 5 (RFC5) in acute myeloid leukemia (AML) from four aspects: expression, prognosis, biological functions, and its effects on the immune system.

Methods

The RFC5 gene expression and survival analyses, biological function analyses including functional enrichment analysis of genes co-expressed with RFC5, RFC5-interacted gene network construction, gene set enrichment analysis (GSEA), and immune infiltration analysis were performed using data based on GDC TCGA and GEO. The CIBERSORT algorithm was employed to quantify immune cell fractions. All the statistical analyses were performed in SPSS software, GraphPad Prism, and R software.

Results

RFC5 expression was abnormally expressed in AML (P <0.05). Notably, differential RFC5 expression was observed among different FAB AML subtypes and hematopoietic lineages (all P <0.05). More importantly, high RFC5 expression served as an independent prognostic factor for the poor overall survival of AML patients (P <0.001). Enrichment analyses revealed that RFC5 was involved in cell cycle-related pathways in AML. CIBERSORT analysis showed high proportions of M2 macrophages in the high RFC5 expression group.

Conclusions

RFC5 might serve as an effective and robust biomarker for the diagnosis and prognosis of AML. RFC5 might be involved in the AML progression via cell cycle regulation. Moreover, the correlation between RFC5 and immune cells might provide potential assistance for AML treatment.

KEYWORDS:

• RFC5
• prognosis
• AML
• immune infiltrates

Introduction

Acute myeloid leukemia (AML) is a heterogeneous and aggressive hematopoietic malignancy, which accounted for about 1% of all cancers [1]. AML is featured with bone marrow failure and impaired hematopoietic function, resulting in fatal consequences because of the clonal expansion of undifferentiated myeloid progenitor cells [2]. In 2020, 19,940 new cases were estimated in the United States, with an age-adjusted annual incidence of 4.3 per 100,000 populations and high mortality [3]. Currently, chemotherapy is still the primary method for AML treatment, whereas 70% of patients who reach remission will eventually suffer from relapse and develop into refractory leukemia, leading to death [4]. In addition, immunophenotyping surfacing type molecules such as CD38, CD34, and CD45 have been demonstrated to assist the diagnosis of AML in recent years [5]. Moreover, chromosome aberration analysis is available for the diagnosis, classification, and prognosis, but approximately 45% of patients lack representative chromosome aberrations [6]. Despite the emergence of new drugs and the progress in the pathogenesis of AML, the prognosis of AML patients remains unfavorable [7]. The 5-year overall survival (OS) of younger AML patients and the elderly were 38% and less than 10%, respectively [8]. Therefore, it is of urgency to develop more sensitive and novel biomarkers for the early diagnosis and effective treatment of AML patients.

Replication factor C (RFC) is a five-subunit complex comprised of RFC1 (140 kDa), RFC2 (40 kDa), RFC3 (38 kDa), RFC4 (37 kDa), and RFC5 (36 kDa) [9], which are highly conserved in all eukaryotes [10–12]. The five subunits have high homology to each other [13]. It has been reported that RFC can load proliferating cell nuclear antigen and DNA polymerase onto a template-primer junction in an ATP-dependent manner [14]. Besides, RFC represents a crucial role in checkpoint control, DNA polymerase switching, and DNA repair [15–17]. As a subunit of RFC, RFC5 is involved in cell cycle regulation, nucleotide excision, DNA double helix damage, and repairing mismatches [18, 19]. A previous study showed significant RFC5 upregulation in prostate cancer compared to normal prostate tissues [20]. In cervical cancer C33A cells, overexpression of SIX1 upregulated the RFC5 expression [21]. Additionally, the elevated RFC5 expression in tumor tissues prior to isolated hepatic perfusion is significantly related to poor prognosis [22]. However, little is known about the connection of RFC5 with AML.

In this study, we evaluated the association of RFC5 expression with clinical characteristics and OS in AML patients using data based on the cancer genome atlas (TCGA) and gene expression omnibus (GEO). Then, functional enrichment analysis was performed, and an RFC5-interacted gene network was constructed to explore the biological functions of RFC5-related genes. Also, gene set enrichment analysis (GSEA) was carried out to investigate the potential regulatory pathway of RFC5 in AML. Finally, we examined a correlation between RFC5 expression and tumor-infiltrating immune cells. This work illustrated the value of RFC5 in AML and determine the potential mechanism of RFC5 in regulating the prognosis of AML patients.

Materials and methods

Data source

The HTSeq-FPKM data and clinical data for AML samples based on GDC TCGA were downloaded from the UCSC Xena (https://xenabrowser.net/datapages/), which has recomputed all raw expression data from TCGA. In this study, clinical data and prognostic information of all available samples were extracted, and the prognostic indicator mainly includes OS. Finally, 132 samples with complete survival and expression data were enrolled. In addition, we used three publicly available datasets from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Of these datasets, GSE30029 includes expression data for AML and normal bone marrow cells, GSE42519 involves expression data for major hematopoietic lineages during the differentiation, and GSE10358 consists of the expression data for primary AML samples.

Expression and survival analysis

Firstly, we adopted the GSE30029 dataset to analyze the RFC5 mRNA expression in AML and normal groups. Besides, the association of OS rates with RFC5 mRNA expression in AML was investigated.

Biological function and pathway analyses

To explore the pathological role of RFC5, the LinkedOmics database (http://linkedomics.org/login.php) was used to obtain the genes co-expressed with RFC5. LinkedOmics is a publicly available portal that includes multi-omics data from all 32 TCGA Cancer types. The selection criterion was as follows: cancer cohort, TCGA_LAML; search dataset, RNAseq; search dataset attribute, RFC5; target dataset, RNAseq; statistical method, Pearson Correlation test. The top 150 identified genes with P-value <0.001 and FDR <0.001 were selected for further gene ontology (GO) annotation and kyoto encyclopedia of genes and genomes (KEGG) pathway analyses. Additionally, GeneMANIA (http://genemania.org/search/homo-sapiens/) was employed to reveal the other essential genes related to RFC5, using physical and genetic interactions, co-expression, co-localization, pathway, prediction, and protein domain similarity.

Further, GSEA was carried out to elucidate the significant survival difference between high and low RFC5 expression groups in AML. To obtain normalized enrichment scores (NES), the nominal p-value and false discovery rate (FDR) q-value were determined. The number of permutations was set to 1,000, and the expression level of RFC5 was used as a phenotypic label. The terms with a nominal p-value <0.05 and an FDR q-value <0.25 were considered as significant pathways.

Immune infiltration analysis

The CIBERSORT (https://cibersort.stanford.edu/index.php), a deconvolution algorithm based on gene expression, was used to measure the immune response of 22 tumor-infiltrating immune cells to analyze their association with RFC5 expression in AML and to uncover correlations between tumor-infiltrating immune cells. More detailed information for this method is available in Newman AM et al.’s research [23].

Statistical analysis

All statistical analyses were performed by SPSS software, version 23.0 (SPSS, Inc., Chicago, IL, USA), GraphPad Prism, version 8.0 (Graphpad Software, La Jolla, CA, USA), and R software. One-way ANOVA was used to test for differences among at least three groups and the t-test was used to assess differences in each two-group comparison. Low and high RFC5 expressers were discriminated against according to the median expression level of RFC5. The relationship between RFC5 and clinicopathological characteristics was determined using logistic regression analysis. The Kaplan-Meier plotter method was employed to construct the survival curves using the log-rank test. Through Cox regression analysis, we predicted the independent prognostic factors for OS of AML patients. Hazard ratio (HR) and 95% confidence interval (CI) were calculated in both univariate and multivariate regression analyses. A nomogram was constructed based on the Cox regression model using the ‘rms’ package in R, and GO and KEGG analyses were performed using the ‘clusterProfiler’ package in R. Whether there was a relationship between immune cells infiltration and RFC5 expression was evaluated by the Pearson Correlation test. All differences were considered statistically significant at the level of P <0.05.

Results

The expression of RFC5 in AML

The RFC5 mRNA expression was significantly lower in AML cells than that in normal cells in the GSE30029 dataset (P  = 5.2e-10) (Figure 1(a)). The distribution of RFC5 mRNA expression, survival status of AML patients, and RFC5 expression profiles were presented in Figure 1(b). In addition, the mRNA expression difference of RFC5 in eight FAB AML subtypes from M0 to M7, and the transcription levels of RFC5 during hematopoiesis differentiation were analyzed with statistical significances (all P <0.05) (Figure 1(c and d)). These results indicated that abnormal expression of RFC5 might facilitate the AML occurrence and development.

Figure 1. RFC5 mRNA expression in acute myeloid leukemia. The RFC5 expression between tumor and normal samples from (A) GSE30029. (B) RFC5 expression distribution and survival status. (C) RFC5 expression in eight FAB AML subtypes. (D) RFC5 expression in different hematopoietic lineages.

Then, the relationship between RFC5 mRNA expression and clinicopathological parameters in AML was evaluated by logistic regression analysis. A total of 132 samples with complete expression and survival information based on GDC TCGA were included. Surprisingly, elevated expression of RFC5 was not significantly related to the clinical features including age, gender, blast count, and platelet count (all P>0.05) (Table 1).

Table 1. Association of RFC5 expression with clinicopathological characteristics using logistic regression in the GDC TCGA cohorts.

Characteristics	Total (N)	Odds ratio in RFC5 expression	P-value
Age	132	0.997 (0.976-1.020)	0.817
Gender	132	1. 1. 611 (0.789-3.290)	0.191
Blast count	132	1. 1. 012 (0.999-1.024)	0.053
Platelet count	132	1.001 (0.994-1.007)	0.783

The prognostic value of RFC5 in AML

We first used the GDC TCGA-LAML data to investigate the influence of RFC5 expression on the prognosis of patients with AML. As shown in Figure 2(a), patients with high RFC5 expression tented to have lower OS time compared with the low expression group (HR = 1.68, P = 0.02). The identical result was obtained in the GSE10358 dataset (HR = 3.59, P = 2.5e-3) (Figure 2(b)). To validate the association of RFC5 expression with AML, the GDC TCGA-LAML data were used for receiver operating characteristics (ROC) analysis. The results showed that the area under curve (AUC) of RFC5 expression for OS was 0.656 (95% CI: 0.559-0.753; P <0.01) (Figure 2(c)), suggesting that RFC5 might be a robust biomarker for distinguishing the survival status of AML patients, which was consistent with the above findings.

Figure 2. The prognostic significance of RFC5 in acute myeloid leukemia. (A) High RFC5 expression predicts poor overall survival of patients using GDC TCGA-LAML data. (B) Verification of the prognostic value of RFC5 using the GSE10358 dataset. (C) Receiver operating characteristics curve using GDC TCGA-LAML data.

Subsequently, Cox regression analysis was performed to examine the independent prognostic value of RFC5 for OS of AML patients in the GDC TCGA cohorts. The univariate analysis result showed that age and RFC5 were significantly linked to patient OS (all P <0.01), while other clinical factors including gender, FAB classifications, blast count, and platelet count had no significant impact on the clinical outcome (Table 2). After multivariate analysis, RFC5 (HR: 1.903; 95%CI: 1.038-3.489; P <0.05), and age (HR: 1.039; 95%CI: 1.022-1.056; P <0.001) were still notably associated with OS (Table 2). The results revealed that RFC5 and age served as independent prognostic factors for OS of AML patients.

Table 2. Cox regression analysis of RFC5 expression and overall survival in the GDC TCGA cohorts.

	Univariate analysis		Multivariate analysis
Characteristics	HR (95% CI)	P-value	HR (95% CI)	P-value
Age	1.034 (1.018-1.051)	<0.001	1.039 (1.022-1.056)	<0.001
Gender	0.962(0.619-1.495)	0.864	0.830 (0.528-1.305)	0.419
FAB classifications (M4-M7 vs. M0-M3)	1.527 (0.966-2.416)	0.070	1.400 (0.868-2.259)	0.168
Blast count	1.003 (0.996-1.010)	0.379	1.008 (0.999-1.016)	0.064
Platelet count	1.001 (0.997-1.004)	0.660	1.002 (0.998-1.006)	0.387
RFC5	2.340 (1.284-4.265)	0.006	1.903 (1.038-3.489)	0.037

Abbreviations: HR, hazard ratio; CI, confidence interval.

To better reveal the prognostic value of RFC5 and age for OS in AML patients, a nomogram was constructed on basis of the Cox regression model. Nomograms of 1-, 3- or 5-year OS in the GDC TCGA cohort were presented in Figure 3(a), showing that age contributed most to the OS of AML patients, followed by RFC5. Moreover, the 1-, 3, and 5-year calibration curves were drawn, exhibiting that the nomogram predicted well (C-index: 0.69; 95% CI: 0.63-0.75; P <0.001) (Figure 3(b)). The above findings indicated that RFC5 played an important role in predicting the OS of AML patients.

Figure 3. Construction of nomogram. (A) Nomogram based on RFC5 and clinical characteristics. (B) The calibration curves.

Biological function and pathway analyses

We then explored the biological functions and potential pathways of RFC5 and its co-expressed genes in AML. All the related co-expressed genes with RFC5 were obtained through LinkedOmics databases and the association result was presented in the volcano plot (Figure 4(a)). Figure 4(b and c) exhibited the top 50 co-expressed genes that were positively and negatively associated with RFC5, respectively. The top 150 co-expressed genes meeting the threshold of P<0.001 and FDR <0.001 were selected for further GO annotation and KEGG pathway analyses. Figure 4(d) indicated that BPs were mainly enriched in the cell cycle, chromosome organization, and DNA metabolic process. The major CCs were chromosome, chromosomal region, and nuclear chromosome (Figure 4(e)). The MFs that RFC5 participated in included adenyl nucleotide binding, ATPase activity, and DNA helicase activity (Figure 4(f)). As for KEGG pathway, genes co-expressed with RFC5 were mainly involved in cell cycle, DNA replication, and mismatch repair (Figure 4(g)).

Figure 4. Identification of genes co-expressed with RFC5 and its functional enrichment analysis. (A) The volcano plot of the association result of RFC5. Top 50 genes that were positively (B) and negatively (C) related to RFC5. (D) Biological process. (E) Cellular components. (F) Molecular functions. (G) KEGG pathway.

Following this, the biological functions of the other important genes most associated with RFC5 were elucidated through the construction of a gene interaction network for RFC5 using GeneMANIA. As shown in Figure 5, a total of 20 relevant genes were generated. The biological processes mainly included cell cycle DNA replication, telomere organization, and nucleotide-excision repair.

Figure 5. RFC5-related genes analysis. Interaction and biological process analyses of RFC5 and its related genes.

Further, GSEA was used to identify signaling pathways involved in AML between low and high RFC5 expression phenotypes. Five pathways including DNA replications, mismatch repair, nucleotide excision repair, cell cycle, and P53 signaling pathway showed significantly differential enrichment in RFC5 high expression phenotype based on NES, the nominal p-value, and FDR q-value (Figure 6) (Table 3).

Figure 6. Enrichment plot from gene set enrichment analysis.

Table 3. Gene sets enriched in the high RFC5 expression phenotype.

Gene set name	NES	Nominal p-value	FDR q-value
DNA replication	−2.1287	<0.001	0.0033
Mismatch repair	−2.1065	0.002	0.0034
Nucleotide excision repair	−2.4984	<0.001	<0.001
Cell cycle	−2.4679	<0.001	<0.001
P53 signaling pathway	−1.7395	0.0203	0.0479

Abbreviations: NES, normalized enrichment score; FDR, false discovery rate.

Combined with the functional enrichment analysis, gene interaction network, and GSEA, RGFC5 was mainly involved in the pathways regulating cell cycle control and processing genetic information, indicating that RFC5 might play an essential role in the growth and development of AML.

Relationship between RFC5 expression and immune cells infiltration

Due to the essential role of tumor-infiltrating lymphocytes in predicting the OS of patients with various cancers [24], we systematically evaluated the relationship between RFC5 mRNA expression and immune cell infiltration by the CIBERSORT algorithm. We found that plasma cells, T cells CD8, T cells regulatory (T regs), and M2 macrophages were significantly changed between high RFC5 and low RFC5 groups (all P <0.05), while B cells naive, B cells memory, T cells CD4 naïve, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, T cells gamma delta, NK cells resting, NK cells activated, monocytes, macrophages M0, macrophages M1, dendritic cells resting, dendritic cells activated, mast cells resting, mast cells activated, eosinophils, and neutrophils were not obviously altered between groups (all P >0.05) (Figure 7(a)). More specifically, the high RFC5 expression group tended to harbor more plasma cells and macrophages, while the low RFC5 expression group exhibited more T cells CD8. Less importantly, T regs were rarely infiltrated in both high and low RFC5 expressed groups.

Figure 7. Correlation between RFRC5 mRNA expression and immune infiltration levels. (A) The varied proportions of 22 CIBERSORT immune cells between high and low RFC5 expression groups. (B) Correlation between RFC5 expression and plasma cells, T cells CD8, Tregs, and macrophages. (C) Heatmap of 22 immune infiltration cells in tumor samples.

Subsequently, a Pearson Correlation analysis between RFC5 expression and immune cells including plasma cells, T cells CD8, Tregs, and macrophages M2 was carried out. As shown in Figure 7(b), plasma cells (Cor  = 0.22, P  = 7.7e-3) and M2 macrophages (Cor  = 0.25, P  = 2.1e-3) had a positive correlation with RFC5 mRNA expression. We also assessed the correlations between 22 types of immune cells (Figure 7(c)). The resulting heat map indicated that the ratios of various tumor-infiltrating immune cells subpopulations were weakly to moderately correlated.

Discussion

AML is a heterogeneous and aggressive hematologic malignancy, characterized by high relapse and low cure rate [25]. AML exhibits complex and diverse genetic changes, contributing to the malignant proliferation of AML cells and variable clinical outcomes of AML patients [26, 27]. These mutated and abnormally expressed genes linked to AML provide important prognostic information for determining chemotherapy response and patient prognosis [28, 29]. However, the advances in AML treatment are still limited, and the current prognostic evaluation can not fully distinguish the prognosis of AML patients [30]. Therefore, developing novel biomarkers and potential therapeutic targets to improve the AML diagnosis and therapy was urgently needed. RFC5 as a clamp loader was abnormally expressed in multiple cancer tissues or cells. Martinez et al. demonstrated that RFC5 was highly expressed in HPV-positive squamous cell carcinoma of the head and neck tissues than that in normal oral mucosal tissues [31]. Also, higher RFC5 expression was observed in the multidrug-resistant leukemia cell line HL-60R [18]. Herein, we investigated the role of RFC5 expression on tumorigenesis, progression, and prognosis of AML by using the data based on GDC TCGA and GEO. Expectedly, abnormal expression of RFC5 was related to the occurrence and development of AML. Interestingly, the logistic analysis showed that RFC5 was not associated with clinical characteristics such as age, gender, and blast count. The survival analysis highlighted the significance of increased RFC5 as an independent prognostic predictor for worse OS of AML patients. Moreover, upregulated RFC5 mRNA expression might have an impact on the mechanisms of tumorigenesis and cancer immunology in AML progression.

To probe into the mechanism of RFC5 in AML, we implemented the enrichment analysis. GO annotation showed that RFC5 was mainly enriched in cell cycle, nuclear chromosome, and adenyl nucleotide binding. The KEGG pathway analysis revealed that RFC5 and its co-expressed genes were mainly involved in cellular processes and genetic information processing including cell cycle, DNA replication, and mismatch repair, and nucleotide excision repair. We next embarked on the construction of the RFC5-interacted gene network and found that RFC5 and its related genes played an essential role in cell cycle DNA replication and nucleotide excision repair. Further, GSEA suggested that high expression of RFC5 could regulate the cell cycle, and process genetic information such as DNA replications, mismatch repair, and nucleotide excision repair in AML patients. Therefore, the authors speculated that RFC5 was crucial in the progression of AML via cell cycle regulation. Our results help to deepen the understanding of the biological functions of RFC5.

Immune cells are key factors in the tumor microenvironment, which could predict the survival status and therapeutic efficacy of cancer patients [32–34]. Importantly, CIBERSORT analysis showed that the plasma cells and macrophages were observed at elevated levels in the high RFC5 expression group with a statistical difference, while levels of T cells CD8 and T regs were significantly decreased. Moreover, Pearson Correlation analysis revealed a strong positive association of RFC5 expression level and infiltration level of plasma cells and M2 macrophages. These findings revealed a possible mechanism where RFC5 regulated the functions of M2 macrophages in AML. M2 macrophages could highly express transforming growth factor β (TGF-β), and interleukin 10 (IL-10), helping tumors escape the host’s immune surveillance [35]. Besides, M2 macrophages could produce chemokines including CCL24, CCL22, and CCL17, which were associated with Th2 response, and further inhibit the inflammatory response [36, 37]. It has been evidenced that AML cells own a stronger ability to chemoattract M2 macrophages in comparison to normal cells [38]. Our results presented that RFC5 overexpression enhanced this ability of AML cells. In addition, high expression of M2 macrophage markers including CD206, CD163 contributed to shorter survival time [38, 39]. This could be an explanation why the OS of AML patients with high RFC5 expression was unfavorable. However, clinical trials are required to verify the correlation between RFC5 and M2 macrophages. Besides, genetic information would be enrolled in the expression and survival analysis if data is available in the future.

In conclusion, the high expression level of RFC5 shortened the OS of AML patients and RFC5 might participate in the progression of AML via regulating cell cycle-related pathways. Moreover, RFC5 mRNA expression was significantly correlated with the infiltration level of immune cells, particularly in M2 macrophages. Thus, RFC5 could serve as a potent biomarker for the diagnosis and treatment of AML.

Declaration

Data Sharing Statement: The datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request.

Ethics approval and consent to participate

Not applicable.

Clinical trials registry

Not applicable.

Authors’ contributions

LW and WD analyzed, designed and interpreted the data. ZY, ZC and LX contributed to the discussion and revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

Disclosure statement

No potential conflict of interest was reported by the author(s).